title: Multiobjective evolutionary optimization for tumor segmentation of breast ultrasound images.
abstract: This paper proposes a robust multiobjective evolutionary algorithm (MOEA) to optimize parameters of tumor segmentation for ultrasound breast images. The proposed algorithm employs efficient schemes for reinforcing proximity to Pareto-optimal and diversity of solutions. They are designed to solve multiobjective problems for segmentation accuracy and speed. First objective is evaluated by difference between the segmented outline and ground truth. Second objective is evaluated by elapsed time during segmentation process. The experimental results show the effectiveness of the proposed algorithm compared with conventional MOEA from the viewpoint of proximity to the Pareto-optimal front (improved by 16.4% and 12.4%). Moreover, segmentation results of proposed algorithm describe faster segmentation speed (1.97 second) and higher accuracy (8% Jaccard).
authors.forename: Ye-Hoon
authors.initials: YH
authors.lastname: Kim
authors.forename: Baek Hwan
authors.initials: BH
authors.lastname: Cho
authors.forename: Yeong Kyeong
authors.initials: YK
authors.lastname: Seong
authors.forename: Moon Ho
authors.initials: MH
authors.lastname: Park
authors.forename: Junghoe
authors.initials: J
authors.lastname: Kim
authors.forename: Sinsang
authors.initials: S
authors.lastname: Yu
authors.forename: Kyoung-Gu
authors.initials: KG
authors.lastname: Woo
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D016022:Case-Control Studies; D002648:Child; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008875:Middle Aged; D056910:Republic of Korea; D014463:Ultrasonography; D055815:Young Adult